Why does fit_transform with OneHotEncoding not maintain the transformation for my X variable?
I am using a pipeline to transform my X variable that has categorical features using OneHotEncoding; however, even after I fit_transfrom X, when I print X, I still see categorical values:
X = df.drop('Loan_Status', axis='columns') y = df['Loan_Status'] from sklearn.compose import make_column_transformer from sklearn.preprocessing import OneHotEncoder column_trans = make_column_transformer( (OneHotEncoder(), ['Loan_ID', 'Gender', 'Married', 'Education', 'Self_Employed','Property_Area']), remainder='passthrough') column_trans.fit_transform(X) Am I missing a step to ensure my X variable maintains the encoded features?
Thanks for your time.
